Skip to main content

The NERD algorithm performs calculations with increased accuracy, displaying results almost in real-time.

Project description

status

NERD: Numerical Estimation of Rodenticide Density

The eradication of rodents is central to island restoration efforts and the aerial broadcast of rodenticide bait is the preferred dispersal method. To improve accuracy and expedite the evaluation of aerial operations, we developed an algorithm for the numerical estimation of rodenticide density (NERD). The NERD algorithm performs calculations with increased accuracy, displaying results almost in real-time. NERD describes the relationship between bait density, the mass flow rate of rodenticide through the bait bucket, and helicopter speed and produces maps of bait density on the ground. NERD also facilitates the planning of helicopter flight paths and allows for the instant identification of areas with low or high bait density.

Installation 🏗️

To install from pip:

pip install geci-nerd

or clone directly from Github

git clone git@github.com:IslasGECI/nerd.git

cd nerd

and install from source

pip install --editable .

geci-nerd is developed under Python >= 3.8. 🐍

Jupyter Notebook Demonstrations 📒

You can explore the functionality of NERD through interactive Jupyter notebooks. These are the options to access the demonstration notebooks:

  • View a static version on GitHub. Simply navigate to the calibration-demo and tiling_demo notebooks.
  • Alternatively, you can run the Jupyter notebooks locally using Docker. Follow the instructions below:

First, pull the latest demo image:

docker pull islasgeci/nerd_demo:latest

Then, run the container:

docker run --detach --publish 8080:8888 --rm islasgeci/nerd_demo

Lastly, explore the Jupyter notebooks at http://localhost:8080/

References ✏️

  • Rojas-Mayoral, E. (2019) «Improving the efficiency of aerial rodent eradications by means of the numerical estimation of rodenticide density». Island invasives: scaling up to meet the challenge, IUCN. doi: 10.5281/zenodo.10214344.

Author

Contributors

Guidelines

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

geci_nerd-0.4.1.tar.gz (10.9 kB view details)

Uploaded Source

Built Distribution

geci_nerd-0.4.1-py3-none-any.whl (26.2 kB view details)

Uploaded Python 3

File details

Details for the file geci_nerd-0.4.1.tar.gz.

File metadata

  • Download URL: geci_nerd-0.4.1.tar.gz
  • Upload date:
  • Size: 10.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for geci_nerd-0.4.1.tar.gz
Algorithm Hash digest
SHA256 1b1d0d69159959d0a455dac0db8870fb67c628b84aa8edf13ba02d34e13d4f0a
MD5 b6770a26dda35cd855bef603655c2444
BLAKE2b-256 243ca555e705011704e60041939239725db96fb232ec63017c529a253836ece2

See more details on using hashes here.

File details

Details for the file geci_nerd-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: geci_nerd-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 26.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for geci_nerd-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 8b4f05dd085a41a592d2c471127335f8c5ee4378ce316bae72e410aa1da2ed86
MD5 a57638ececc696c534e6d1f9f0b7fdbf
BLAKE2b-256 d391ee058e50fb413246e1d60e7711ee04030ba91bb37067aa78a1d15974ba0a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page